Wavelet detector for finding similarities between major boundaries in images

ABSTRACT

A method of and a system for finding similarities between major boundaries of images using a wavelet detector is described herein. Unimportant edges of the image are disregarded by eliminating Gaussian wavelet coefficients and Haar wavelet coefficients of lower significance. Comparison between the images is made on the basis of quantized color, sign and magnitude of the Haar wavelet coefficients. The method performs the comparison between images in two steps. First, the method checks for exact matches between the Haar wavelet coefficients to determine whether the images are very similar. This is followed by binning of the coefficients into nine spatial bins in the image. A representative is assigned to each of the bins in terms of color, orientation and sign. Each bin of one image is compared with all the bins of the other image. Thus, images that are similar but not identical are still detected.

This Patent Application is a divisional of co-pending U.S. patentapplication Ser. No. 11/698,485, filed on Jan. 25, 2007, and entitled“Wavelet Detector For Finding Similarities Between Major Boundaries InImages.” The patent application Ser. No. 11/698,485, filed on Jan. 25,2007, and entitled “Wavelet Detector For Finding Similarities BetweenMajor Boundaries In Images,” is hereby incorporated by reference.

FIELD OF THE INVENTION

The present invention relates to the field of imaging. Morespecifically, the present invention relates to detecting boundaries inimages.

BACKGROUND OF THE INVENTION

Images that are not simply one color contain at least one edge andusually many edges. Edges are abrupt changes in the brightness of theimage. For example, if an image contains a red airplane in a gray andcloudy sky, there are edges where the airplane outline meets the bluesky. Since the brightness distinction between these two objects islikely very high, the edges are considered to be strong. However, forthe gray-white clouds and the gray sky, there is still an edge at theborder of each cloud, but they will likely be considered weak since theyare not very distinct. Since edges are able to be representedmathematically, they are able to be used in image comparison as well asfor other image based purposes.

Mallat and Zhong teach a method of edge-wavelet multiresolution analysisfor image representation and compression in their paper,“Characterization of signals from multiscale edges,” published in IEEETransactions on Pattern Analysis and Machine Intelligence, 14, 7,710-732, 1992. Image compression is achieved in their work by findingand keeping only the local extrema in their edge-wavelet coefficients.Finding local extrema of image gradients as an edge detector is commonlyknown as Canny's edge detector proposed by F. Canny, in his paper, “Acomputational approach to edge detection,” IEEE Transactions on PatternAnalysis and Machine Intelligence, vol. PAMI-8, 6, 679-698, November1986. Mallat and Zhong also teach a method of image reconstruction fromedge-wavelet extrema by iterative projection onto feasible solutionsets. The iterative projection method is too computationally intensive,and hence too time-consuming to be of practical use on commonlyavailable image processing computers such as personal computers. Itcurrently takes several hours to process a single image using theiterative projection method.

Previous attempts at image comparison/matching have many drawbacks. Manyprior attempts did not take a transform of the image; they simply tookthe sign of coefficients from the images and then compared the signs oftwo images adding to a running total for every match to achieve a score.Depending on the score, there either was or was not a match. In additionto being computationally expensive, typically they focus on one aspectof the images and ignore the rest. For example, the method describedabove by Mallat and Zhong focuses only on edges of the images. Thus, iftwo images have similar edges, they are viewed as matching. However,there are clearly problems with only using the edges for matching. Forinstance, a person's head and a volleyball are both round and similar insize, thus will have similar edges. However, when a person is searchingfor an image of a volleyball, the search would be ineffective if imagesof people interfered with the search.

SUMMARY OF THE INVENTION

A method of and a system for finding similarities between majorboundaries of images using a wavelet detector is described herein.Unimportant edges of the image are disregarded by eliminating Gaussianwavelet coefficients and Haar wavelet coefficients of lowersignificance. Comparison between the images is made on the basis ofquantized color, sign and magnitude of the Haar wavelet coefficients.The method performs the comparison between images in two steps. First,the method checks for exact matches between the Haar waveletcoefficients to determine whether the images are very similar This isfollowed by binning of the coefficients into nine spatial bins in theimage. A representative is assigned to each of the bins in terms ofcolor, orientation and sign. Each bin of one image is compared with allthe bins of the other image. Thus, images that are similar but notidentical are still detected.

In one aspect, a method of determining edges of an image comprisescalculating a Gaussian wavelet coefficient of the image, retaining theGaussian wavelet coefficient, wherein the Gaussian wavelet coefficientis above a first threshold, calculating a Haar wavelet coefficient ofthe image, retaining the Haar wavelet coefficient, wherein the Haarwavelet coefficient is co-positioned near a non-zero Gaussian waveletcoefficient and calculating a color of an edge represented by theGaussian wavelet coefficient and the Haar wavelet coefficient. Themethod further comprises retaining the Gaussian wavelet coefficient,wherein the Gaussian wavelet coefficient is not above the firstthreshold, but is a neighbor of a retained coefficient and is above asecond threshold, wherein the second threshold is lower than the firstthreshold. The method further comprises discarding the Gaussian waveletcoefficient, wherein the Gaussian wavelet coefficient is below the firstthreshold and is not a neighbor of a retained coefficient. The methodfurther comprises discarding the Gaussian wavelet coefficient, whereinthe Gaussian wavelet coefficient is below the first threshold and is aneighbor of a retained coefficient but is not above a second threshold,wherein the second threshold is lower than the first threshold. Themethod further comprises discarding the Haar wavelet coefficient,wherein the Haar wavelet coefficient is co-positioned near a non-zeroGaussian wavelet coefficient. The Haar wavelet coefficient is retainedby storing a corresponding quantized color, sign and magnitude.

In another aspect, a method of comparing two or more images comprisesdetermining if a match exists between a first set of coefficients of afirst image and a second set of coefficients of an additional image,binning the first set of coefficients and the second set of coefficientseach into N bins if there is not the match between the first set ofcoefficients and the second set of coefficients and performing aregional comparison of the first set of coefficients and the second setof coefficients using the bins. The method includes N is equal to 9.Performing the regional comparison of the first set of coefficients andthe second set of coefficients using the bins includes comparing eachbin from the first image with each bin from the additional image. Themethod further comprises calculating a Gaussian wavelet transform of thefirst image and the additional image, calculating a Haar wavelettransform of the first image and the additional image and calculating areduced color version of the first image and the additional image. Thefirst set of coefficients and the second set of coefficients are Haarwavelet coefficients. The Haar wavelet coefficients are retained bystoring a representative quantized color, sign and magnitude. Thequantized color, sign and magnitude are compared for each of the firstset of coefficients and the second set of coefficients.

In another aspect, a method of locating one or more images comprisesselecting a first image to search for, comparing the first image with anadditional image, wherein comparing comprises: determining if a matchexists between a first set of coefficients of the first image and asecond set of coefficients the additional image, binning the first setof coefficients and the second set of coefficients each into N bins ifthere is not the match between the first set of coefficients and thesecond set of coefficients and performing a regional comparison of thefirst set of coefficients and the second set of coefficients using thebins and retrieving the additional image that is similar to the firstimage. The method includes N is equal to 9. Performing the regionalcomparison of the coefficients using the bins includes comparing eachbin from the first image with each bin from the additional image.Comparing further comprises calculating a Gaussian wavelet transform ofthe first image and the additional image calculating a Haar wavelettransform of the first image and the additional image and calculating areduced color version of the first image and the additional image.Selecting comprises entering in a keyword upon which an image matchingthat keyword is utilized to compare. Selecting is performed by a user.The first set of coefficients and the second set of coefficients areHaar wavelet coefficients. The Haar wavelet coefficients are retained bystoring a representative quantized color, sign and magnitude. Thequantized color, sign and magnitude are compared for each of the firstset of coefficients and the second set of coefficients. Similaritybetween the first image and the additional image depends on a scoreestablished by the Haar wavelet coefficients. Comparing and retrievingoccurs via the Internet. Comparing and retrieving occurs on a computingdevice selected from the group consisting of a personal computer,laptop, digital camera, digital camcorder, handheld, iPod® and homeentertainment system.

In another aspect, a system for locating data comprises an image, one ormore sets of data and a program to search for the image wherein thesearch comprises: determining if a match exists between a first set ofcoefficients of the image and a second set of coefficients the one ormore sets of data, binning the first set of coefficients and the secondset of coefficients each into N bins if there is not a match between thefirst set of coefficients and the second set of coefficients andperforming regional comparison of the first set of coefficients and thesecond set of coefficients using the bins. The system includes N isequal to 9. Performing regional comparison of the coefficients using thebins includes comparing each bin from the image with each bin from theone or more sets of data. Comparing further comprises calculating aGaussian wavelet transform of the image and the one or more sets ofdata, calculating a Haar wavelet transform of the image and the one ormore sets of data and calculating a reduced color version of the imageand the one or more sets of data. The one or more sets of data areselected from the group consisting of images and videos. The programuses a keyword to determine the image to compare. The program uses theimage selected by a user to compare. The program retrieves the one ormore sets of data which are similar to the image. The program lists theone or more sets of data according to similarity to the image. The firstset of coefficients and the second set of coefficients are Haar waveletcoefficients. The Haar wavelet coefficients are retained by storing arepresentative quantized color, sign and magnitude. The quantized color,sign and magnitude are compared for each of the first set ofcoefficients and the second set of coefficients. The similarity betweenthe first image and the one or more sets of data depends on a scoreestablished by the Haar wavelet coefficients. The program searches overthe Internet. The program searches on a computing device selected fromthe group consisting of a personal computer, laptop, digital camera,digital camcorder, handheld, iPod® and home entertainment system.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 illustrates a flowchart of a process of generating a descriptionof an image.

FIG. 2 illustrates a flowchart of a process of determining strong edgesof images.

FIG. 3 illustrates a flowchart of a process of comparing two images.

FIG. 4 illustrates a block diagram of a media storage device with anexternal controller implementing the wavelet detector.

FIG. 5 illustrates a flowchart showing the steps implemented by thecontroller and the media storage device during processing of a contentstream to generate an index database.

FIG. 6 illustrates a flowchart showing the steps implemented by thecontroller and the media storage device during playback of a contentstream.

DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENT

A wavelet detector is described herein as a software program. Thoseskilled in the art will readily recognize that the equivalent of suchsoftware is able to also be constructed in hardware and/or firmware.Since image manipulation algorithms and systems are well known, thepresent description is directed in particular to algorithms and systemsforming part of, or cooperating more directly with, the wavelet detectordescribed herein. Other aspects of such algorithms and systems, andhardware and/or software for producing and otherwise processing theimage signals involved therewith, not specifically shown or describedherein may be selected from such systems, algorithms, components andelements known in the art. Given the method as described according tothe invention in the following materials, software not specificallyshown or described herein that is useful for implementation of theinvention is conventional and within the ordinary skill in such arts.

Still further, as used herein, the software program is able to be storedin a computer readable storage medium, which may include, for example;magnetic storage media such as a magnetic disk (such as a floppy disk)or magnetic tape; optical storage media such as an optical disc, opticaltape, or machine readable bar code; solid state electronic storagedevices such as random access memory (RAM), or read only memory (ROM);or any other physical device or medium employed to store a computerprogram.

A method of and an apparatus for finding similarities between majorboundaries of images using a wavelet detector is described herein.Unimportant edges of the image are disregarded by eliminating Gaussianwavelet coefficients and Haar wavelet coefficients that do not meetcertain requirements. The Gaussian wavelet coefficients are retainedonly if they are above a certain threshold value or if they areneighbors of a coefficient that is retained and are above a lowerthreshold. Coefficients from the Haar wavelet transform are retained ifthey are positioned near a non-zero coefficient of the Gaussian wavelettransform, wherein near refers to being proximate within a Gaussianwavelet/Lipshitz exponent chart. Comparison between the images is madeon the basis of quantized color, sign and magnitude of the Haar waveletcoefficients. By using color as a criterion for comparison, the methodeliminates chances of matchups between edges of different objects in oneimage and the edge of a single object in the other. The method performsthe comparison between images in two steps. First, the method checks forexact matches between the Haar wavelet coefficients to determine whetherthe images are very similar. This is followed by binning of thecoefficients into nine spatial bins in the image. A representative isassigned to each of the bins in terms of color, orientation and sign.The method compares each bin of one image with all the bins of the otherimage. This allows detecting images that are similar in layout but notidentical in positioning of the objects in the images.

The process begins with a description of an image being generated bycalculating the following: 1) an undecimated Gaussian wavelet transformof the image, 2) a decimated Haar transform of the image and 3) areduced color version of the image. Then, curves in the undecimatedwavelets that have a sufficiently low Lipschitz exponent are found, orpreviously found curves are continued. The Lipschitz exponent iscalculated by the strength of the undecimated wavelets over severalscales. The Haar coefficients that do not coincide with the curvescalculated by the Lipschitz exponent are suppressed. By suppressingcertain coefficients, noise and other issues are eliminated. The colorof the edges represented by the coefficients are also calculated. Thestrongest Haar coefficients are coded with their sign, their logarithmand the color. Nine spatial bins similar to a tic-tac-toe board are usedto separate the image into multiple segments. The quantity of thesecoefficients in each of the nine spatial bins in the image are found andcompared with those similarly calculated for another image. The resultis a robust feature descriptor that respects color boundaries, respectschange in position and size, and compares images based on the sharpestand longest lines in the image.

Several issues of standard wavelet feature detectors are addressed:position invariance and getting rid of spurious object boundaries formedby several objects. Lipschitz exponents are used to find strong edges,as they were used in Mallet and Zhong. However, by abbreviating theprocess and not doing the steepest descent processing, the process issped up significantly. Furthermore, the results are combined with colortagging. Haar wavelets are binned to be able to compare images if anobject in the image has moved.

The wavelet detector described herein addresses several concerns withwavelet-based feature detectors. Unimportant edges and noise are weededout using a variation on the method of Mallet and Zhong. The image isfiltered using a fast Infinite Impulse Response (IIR) filter thatcalculates a continuous Gaussian wavelet transformation of the image.All derivatives of the Gaussian are suitable as wavelet bases. Thestandard deviation of the Gaussian chosen is a parameter for optimizingthe performance of this part of the feature detector.

A variation on the Lipschitz exponent calculation is done here. Theundecimated wavelets are calculated at several scales. Then the valuesat the different scales are compared while the tests are done tocontinue the edge (location of proper neighbors using Bresenham's circlealgorithm). If the undecimated coefficients pass the test, the Haarcoefficients are kept. Wavelet coefficients are kept if they are above acertain threshold, or if they are neighbors of a coefficient that isbeing kept, and are above a lower threshold. The threshold is able to belower than the previous threshold because it is a neighbor of acoefficient that is already being kept. A Haar wavelet transform of theimage is also done, and coefficients are kept from the Haar transform ifthey are co-positioned near a non-zero coefficient of the Gaussian. Thecoefficient is kept by storing its quantized color, sign and magnitude.

After the coefficients are determined, the images are compared in twoways. 1) Exact matches between coefficients are searched for which givesa measure for determining when two images are very similar, and they areweighted for the subband of the wavelet. This ensures that thecomparison is a distance measure, such that it will automatically choosean exact match as the best possible score. 2) The coefficients arebinned into nine bins. Each bin is assigned a representative in terms ofcolor, orientation (e.g. magnitude of three wavelet components) andsign. Using the bins, each section of an image is able to be compared toeach section of another image, thus if an object is simply moved, butstill within the image, this will be detected. For example, there aretwo pictures of people playing volleyball, one where the volleyball ison one side of the net and the second image with the volleyball on theother side of the net. If a straight image comparison were used, theimages would likely not match up because the volleyball edges are invery different locations. However, utilizing the bins, the volleyball isin bin 1 in the first image and bin 3 in the second image, and those twobins are compared, thus the image will be correctly matched.

The process is very efficient to calculate, since each storedcoefficient is stored in a double word, and since the first compare isonly on existing coefficients for an exact match. Since the secondcompare requires an averaging step and then a 9-fold compare, it is alsoefficient.

Many improvements are realized by implementing the process describedherein.

1) The straight wavelet feature detector is corrected by getting rid ofedges that are not relevant to major shapes. The combination of theGaussian wavelet cleaning and the Haar cleaning done with the Gaussianwavelets achieves this.

2) Edges of similar color are correlated, which eliminates chancematchups between edges of different objects in one image and the edge ofa single object in the other.

3) By mixing the results of an exact match comparison with a regionalcomparison (on the nine bins), images are allowed if they haverepositioned objects, or are only similar in layout but not identical inpositioning to find each other. This does not happen with straightwavelet feature detectors because wavelets are notoriously not positioninvariant. Position invariance is also derived from the Gaussian wavelettransform. Since this is a continuous wavelet transform and undecimated,it is position invariant. Thus, representations that are closer thanthey would be in a straight wavelet detector or at least comparable areobtained.

The feature detector is able to be used for most Content Based ImageRetrieval (CBIR) tasks. Given suitable processing speed, the featuredetector is also able to be used to perform content retrieval on videosequences by keyframe. The feature detector is also able to be used tobuild filters to find or filter out image types that are eitherdesirable or undesirable (for example, adult content filtering).

FIG. 1 illustrates a flowchart of a process of generating a descriptionof an image. In the step 100, an undecimated Gaussian wavelet of theimage is calculated. In the step 102, a decimated Haar wavelet of theimage is calculated. In the step 104, a reduced color version of theimage is calculated. Preferably, the reduced color version is 16 colorsand 4 bits.

FIG. 2 illustrates a flowchart of a process of determining strong edgesof images. In the step 200, a wavelet transform of an image iscalculated. Preferably, the wavelet transform is an undecimated Gaussianwavelet transform. In the step 202, Lipschitz exponents are calculatedto determine which wavelet transforms are kept and which are discarded.Wavelet transforms that are above a threshold represent sufficientlystrong edges. If, in the step 202, the Gaussian wavelet coefficient isabove a threshold related to the Lipschitz exponents, then it isdetermined if the next coefficient is also above the threshold in thestep 207. If the next coefficient is also above the threshold, then theloop continues at the step 202 until a desired number of coefficientsare checked for edge sharpness and then the coefficient is retained inthe step 208. If, in the step 202, the Gaussian wavelet coefficient isnot above the threshold, then it is determined if the coefficient is aneighbor of a coefficient that is already retained, in the step 204.Also, if in the step 207, the next coefficient is not above thethreshold, then it is determined if the coefficient is a neighbor of acoefficient that is already retained, in the step 204. If, in the step204, the coefficient is not a neighbor of a previously retainedcoefficient, then the coefficient is discarded, in the step 206. In thestep 204, if the coefficient does neighbor a retained coefficient, thenit is determined if the coefficient is above a lower threshold in thestep 210. If the coefficient is not above a lower threshold in the step210, then the coefficient is discarded, in the step 206. If thecoefficient is above a lower threshold in the step 210, then thecoefficient is retained, in the step 208. Then, in the step 212, a Haarwavelet coefficient is calculated using the Gaussian waveletcoefficient. Preferably, the Haar transform is a decimated Haartransform. In the step 214, it is determined if the Haar waveletcoefficient is co-positioned near a non-zero coefficient of the Gaussianwavelet transform. If the Haar wavelet coefficient is not co-positionednear a non-zero Gaussian wavelet coefficient, then the Haar waveletcoefficient is discarded in the step 216. If the Haar waveletcoefficient is co-positioned near a non-zero Gaussian waveletcoefficient, then the Haar wavelet coefficient is retained in the step220, after the color of the edges represented by the coefficients arecalculated in the step 218. The Haar wavelet coefficient is retained bystoring its quantized color, sign and quantized magnitude.

FIG. 3 illustrates a flowchart of a process of comparing two images: animage to be recognized and a possibly matching image. In the step 300,it is determined if there is an exact match between coefficientsdetermined above. If there is an exact match between the coefficients inthe step 300, then the matching image is selected or presented dependingon the implementation, in the step 302. If there was not an exact matchin the step 300, then the coefficients are binned into N bins, in thestep 304. Preferably, N is equal to nine. In the step 306, regionalcomparisons are performed using the N bins. In the step 308, if there isa match using the nine bins, then the matching image is selected orpresented in the step 310. If there is no match using the N bins in thestep 306, then the process finishes without finding a match. In someembodiments, an absence of a match is indicated.

One of the applications the wavelet detector described herein is able tobe utilized for is Content-Based Image Retrieval (CBIR) also known asQuery By Image Content (QBIC) and Content-Based Visual InformationRetrieval (CBVIR). CBIR is the application of computer vision to theimage retrieval problem of searching for digital images in largedatabases. “Content-based” means that the search uses the contents ofthe images themselves, rather than relying on metadata such as titles,captions or keywords. CBIR is needed and useful because of thelimitations in metadata-based systems in addition to the increasedbandwidth and processing power of the Internet. Textual informationabout images is easily searched using current technology, but requiresthose descriptions to be input by someone, which is highly burdensomeand impractical when dealing with extremely large amounts of data.Furthermore, keyword searches for text have their own drawbacks such asrequiring a user to accurately phrase his search, otherwise the searchcould result in nothing found.

CBIR systems are implemented in a number of different ways. One examplepermits a user to make a request, similar to a keyword search, such as“rabbit” and any images of rabbits are retrieved. However, unlike akeyword search where the word “rabbit” is searched for, the search looksfor matching characteristics of an image that has a rabbit. Othersystems search for texture, color and shape or even faces. The searchcould begin with a sample image provided by the user or viaspecifications of color schemes and textures. The results are returnedin a variety of ways, and in some embodiments, they are sorted inascending order starting with the smallest distance which correlates tothe closest match. Another method of returning results only returnsthose images whose distance falls within a designated acceptable range.Of course, the accuracy of the search depends on how well the technologyis able to match the user's image with those in the database. Thewavelet detector is able to improve accuracy of a user's search asdescribed above.

Alternatively, instead of the search being across the Internet, CBIRimplementing the wavelet detector is performed on a local intranet oreven on a user's computing device such as a personal computer, laptop,digital camera, digital camcorder, handheld, iPod® and homeentertainment system. For example, if a user wants to find all of theirbaby pictures on the computer, they are able to use the aforementionedtechnologies and retrieve all pictures that resemble a baby.

Another application the wavelet detector is utilized with is a contentrecognition system. The content recognition system for indexingoccurrences of objects within an audio/video content data streamprocesses the stream of data to generate a content index databasecorresponding to the content stream. The content stream is processed byapplying recognition technology utilizing the wavelet detector to thecontent within the content stream to identify and index occurrences ofidentified objects. In an embodiment, the content stream is processed asthe content stream is stored within a media storage device.Alternatively, the content stream is processed after the content streamis stored within the media storage device. The objects that are includedwithin the index database, are identified dynamically by the recognitiontechnology using the wavelet detector during processing. As the contentstream is processed, an entry for each object is generated within theindex database. In some embodiments, each entry includes an objectidentifier and corresponding locations of that object. The locationsreference where the particular content is stored within the mediastorage device. Once the content index database is generated, it is ableto then be used to quickly locate and navigate to specific occurrencesof content and objects within the content stream. The objects that areable to be identified and indexed include any identifiable informationwithin a content stream, including shapes, objects, events and movementswithin video streams. In some embodiments, the content index database isstored on the same media storage device as the content stream.

A media storage device with external controller is illustrated in FIG.4. The media storage device 400 includes an interface circuit 402 forsending communications to and receiving communications from otherdevices coupled to the media storage device 400. The interface circuit402 is coupled to a buffer controller 404. The buffer controller 404 isalso coupled to a RAM 406 and to a read/write channel circuit 408. Theread/write channel circuit 408 is coupled to media 410 on which data isstored within the media storage device 400. The read/write channelcircuit 408 controls the storage operations on the media 410, includingreading data from the media 410 and writing data to the media 410. Anexternal controller 420 is coupled to the buffer controller 404 forcontrolling the processing, classifying and indexing of data streamsstored on the media 410.

As the stream is processed, the recognition engine using the waveletdetector within the controller 420 analyzes the content within thecontent stream to identify the appropriate objects within the contentstream. As described above, the appropriate objects are dynamicallyidentified by the recognition engine during processing. As appropriateobjects within the content stream are identified, the occurrence ofthose identified objects within the content stream is then recordedwithin an index database. Once the content stream is processed and theindex database is generated, the user then has the capability to jump tolocations within the content stream where the desired object occurs, forviewing or editing the content stream.

A flowchart showing the steps implemented in some embodiments by thecontroller 420 and the media storage device 400 during processing of acontent stream to generate an index database is illustrated in FIG. 5.The process starts at the step 500. At the step 502, the objects to beindexed and included in the index database are identified. As describedabove, this identification is performed manually by the user ordynamically by the recognition technology using the wavelet detectorduring processing. At the step 504, the recognition engine orrecognition technology is then applied to the content stream to analyzethe content stream and determine the occurrence of identified objectswithin the content stream.

At the step 506, it is determined whether the content within the contentstream that is currently being analyzed includes an identified object.If the content currently being analyzed does include an identifiedobject, then at the step 508, an entry is generated for the indexdatabase, including the object identifier entry within the objectcategory and an entry identifying the corresponding location of thecontent within the location category. After the generation of the entryfor the index database at the step 508, or if it is determined at thestep 506, that the content currently being analyzed does not include anidentified object, it is then determined at the step 510, if there ismore content within the content stream, or if this is the end of thecontent stream. If it is determined that the content stream has not yetbeen fully processed, then the process jumps back to the step 504, tocontinue processing the content stream. If it is determined at the step510 that all of the content stream has been processed, then the processends at the step 512.

A flowchart showing the steps implemented in some embodiments by thecontroller 420 and the media storage device 400 during playback of acontent stream, that has a corresponding index database, is illustratedin FIG. 6. The process starts at the step 600. At the step 602, a useridentifies an object that they would like to locate within the contentstream. At the step 604, the entry corresponding to the identifiedobject is located within the index database and the location of thefirst occurrence of the object is targeted, using the entries from theobject category and the location category. At the step 606, the firstoccurrence of the object is located within the content stream. At thestep 608, this occurrence of the object is then played back for theuser. At the step 610, it is then determined if the user wants the nextoccurrence of the object located and played back. If the user does wantthe next occurrence of the object located and played back, then the nextoccurrence of the object is located at the step 612. The process thenjumps to the step 608 to playback this next occurrence. If it isdetermined at the step 610 that the user does not want the nextoccurrence of the object located and played back, the process then endsat the step 614.

As an example of the operation of the content recognition system andindex database of the present invention, a user records a video of theirchild's birthday on a tape within a video recorder. This video includesaudio and video components. The video is then recorded from the tape toa media storage device 400. Under the control of the controller 420 inconjunction with the media storage device 400, the video is processed togenerate the index database by applying recognition technology includingthe wavelet detector to the video components to determine eachoccurrence of an identified object within the content stream. Asdescribed above, this processing occurs either as the video is recordedon the media storage device 400, if the user's system has the processingcapability to perform the processing online, or after the video isstored on the media storage device 400. During processing the video isanalyzed to determine each occurrence of an identified object. As anoccurrence of an identified object is found within the video, an entrycorresponding to that occurrence is then added to the index database.For example, if the user identifies that they want every occurrence of abirthday cake within the video indexed, the recognition technology isthen applied to the video content stream to determine every occurrenceof the birthday cake within the video. These occurrences are identifiedand indexed within the index database, as described above. If the userthen wants to view these occurrences or edit the video based on theseoccurrences, the system will utilize the index database to playbackthese occurrences of the birthday cake within the video or edit thevideo based on the occurrences of the birthday cake within the video.

Alternatively, instead of generating an index database, a search systemis implemented so that a user is able to request a search for somethinglike a birthday cake, the system searches through the video and theimages/video involving a birthday cake are queued to be viewed.

The wavelet detector described herein is utilized in a number ofapplications but generally is utilized to efficiently find edges inimages and determine similarities between two or more images.Furthermore, unlike previous attempts, the wavelet detector hereinutilizes Gaussian wavelet coefficients, Haar wavelet coefficients andcolor information so that quantized color, sign and magnitude are usedto compare images. By using more data, better comparisons are possible.By providing better image recognition, the wavelet detector hereinimproves the abilities of any implementation that requires imagecomparison.

In operation the wavelet detector improves the image comparisoncapabilities of whatever system it is utilized with. As shown above,when two images are similar to the human eye, but are technicallydissimilar because of mathematical idiosyncrasies, the wavelet detectoris able to much better compare the images like a human would. Whenimplemented with the aforementioned technologies or any other technologythat would benefit from the wavelet detector, the wavelet detectorfunctions by comparing color, sign and magnitude information fromdetermined using Gaussian wavelet coefficients, Lipschitz exponents andHaar wavelet coefficients. For example, when a user performs an imagesearch and selects an image to find, the wavelet detector ensures thatthe search results in the most closely related images. The waveletdetector described herein is an extremely useful addition to any toolimplementing image comparison.

The present invention has been described in terms of specificembodiments incorporating details to facilitate the understanding ofprinciples of construction and operation of the invention. Suchreference herein to specific embodiments and details thereof is notintended to limit the scope of the claims appended hereto. It will bereadily apparent to one skilled in the art that other variousmodifications may be made in the embodiment chosen for illustrationwithout departing from the spirit and scope of the invention as definedby the claims.

1. A method of determining edges of an image comprising: a. calculatinga Gaussian wavelet coefficient of the image; b. retaining the Gaussianwavelet coefficient, wherein the Gaussian wavelet coefficient is above afirst threshold; c. calculating a Haar wavelet coefficient of the image;d. retaining the Haar wavelet coefficient, wherein the Haar waveletcoefficient is co-positioned near a non-zero Gaussian waveletcoefficient; and e. calculating a color of an edge represented by theGaussian wavelet coefficient and the Haar wavelet coefficient.
 2. Themethod as claimed in claim 1 further comprising retaining the Gaussianwavelet coefficient, wherein the Gaussian wavelet coefficient is notabove the first threshold, but is a neighbor of a retained coefficientand is above a second threshold, wherein the second threshold is lowerthan the first threshold.
 3. The method as claimed in claim 1 furthercomprising discarding the Gaussian wavelet coefficient, wherein theGaussian wavelet coefficient is below the first threshold and is not aneighbor of a retained coefficient.
 4. The method as claimed in claim 1further comprising discarding the Gaussian wavelet coefficient, whereinthe Gaussian wavelet coefficient is below the first threshold and is aneighbor of a retained coefficient but is not above a second threshold,wherein the second threshold is lower than the first threshold.
 5. Themethod as claimed in claim 1 further comprising discarding the Haarwavelet coefficient, wherein the Haar wavelet coefficient isco-positioned near a non-zero Gaussian wavelet coefficient.
 6. Themethod as claimed in claim 1 wherein the Haar wavelet coefficient isretained by storing a corresponding quantized color, sign and magnitude.